subject:"\[GitHub\] spark pull request #14872\: \[SPARK\-3162\]\[MLlib\]\[WIP\] Add local tree training ..."

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

2017-01-23 Thread smurching

Github user smurching closed the pull request at:

https://github.com/apache/spark/pull/14872


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[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

2016-09-27 Thread smurching

GitHub user smurching reopened a pull request:

https://github.com/apache/spark/pull/14872

[SPARK-3162][MLlib][WIP] Add local tree training for decision tree 
regressors

## What changes were proposed in this pull request?

Based on [Yggdrasil](https://github.com/fabuzaid21/yggdrasil), added local 
training of decision tree regressors.

Some classes/objects largely correspond to Yggdrasil classes/objects.
Specifically:
* class LocalDecisionTreeRegressor --> class YggdrasilRegressor
* object LocalDecisionTree --> object YggdrasilRegression
* object LocalDecisionTreeUtils --> object Yggdrasil

## How was this patch tested?

Added unit tests in (ml/tree/impl/LocalTreeTrainingSuite.scala) verifying 
that local & distributed training of a decision tree regressor produces the 
same tree.



You can merge this pull request into a Git repository by running:

$ git pull https://github.com/smurching/spark local-trees-pr

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/spark/pull/14872.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #14872


commit acf5b3e29a346a0cb86f621269855a6a98a9a74e
Author: Siddharth Murching 
Date:   2016-08-29T23:51:33Z

Add local tree training for decision tree regressors

commit aa4fcc8d401385f38fe0cdfdb9fe39062c3a9f96
Author: Siddharth Murching 
Date:   2016-08-30T01:19:07Z

Fix setting of impurity values for leaf nodes to match values produced by
distributed Random Forest algorithm

commit f273fc6a4b5048ae577d03676def354dce5c87a7
Author: Siddharth Murching 
Date:   2016-08-31T07:01:26Z

WIP refactoring single-machine tree code

commit 5e61e3b29c236d27e0d655d15a48f2fe3e13d26a
Author: Siddharth Murching 
Date:   2016-09-01T01:22:48Z

Remove unused imports, remove array of single-node impurity aggregators

commit d2060fc460a97228a36bf81956cf8dd24c83106e
Author: Siddharth Murching 
Date:   2016-09-01T23:11:17Z

WIP

commit 634a3223374608d68018daac5500a429034bbc20
Author: Siddharth Murching 
Date:   2016-09-02T00:21:10Z

More work, tests still pass

commit eb7fde00e0db5aa5d04951f8f4a9cd62204f1609
Author: Siddharth Murching 
Date:   2016-09-02T17:02:06Z

WIP: Added tests for classes upon which local tree training is dependent. 
Some integration tests fail

commit b748f05e3eaa7d58b1ad86d269e0dda5f35ee885
Author: Siddharth Murching 
Date:   2016-09-02T17:37:31Z

WIP debugging

commit 297052242727e6693ccbacf89f44b3ff6db584f7
Author: Siddharth Murching 
Date:   2016-09-02T21:34:13Z

Consolidate checking for valid splits

commit 8d443ce38f958e7b83b502e614e01c824cb63c4b
Author: Siddharth Murching 
Date:   2016-09-02T21:52:47Z

Delete empty test suite

commit ee56ffe98756ed78cefbc3f782a471f04e80b256
Author: Siddharth Murching 
Date:   2016-09-02T22:49:38Z

Fix some style errors




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[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

2016-09-02 Thread smurching

Github user smurching closed the pull request at:

https://github.com/apache/spark/pull/14872


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[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

2016-08-30 Thread jkbradley

Github user jkbradley commented on a diff in the pull request:

https://github.com/apache/spark/pull/14872#discussion_r76853720
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/tree/impl/PartitionInfo.scala ---
@@ -0,0 +1,422 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.apache.spark.util.collection.BitSet
+
+/**
+ * Intermediate data stored during learning.
+ * TODO(smurching): Rename; maybe TrainingInfo?
+ *
+ * Node indexing for nodeOffsets, activeNodes:
+ * Nodes are indexed left-to-right along the periphery of the tree, with 
0-based indices.
+ * The periphery is the set of leaf nodes (active and inactive).
+ *
+ * @param columns  Array of columns.
+ * Each column is sorted first by nodes (left-to-right 
along the tree periphery);
+ * all columns share this first level of sorting.
+ * Within each node's group, each column is sorted based 
on feature value;
+ * this second level of sorting differs across columns.
+ * @param nodeOffsets  Offsets into the columns indicating the first level 
of sorting (by node).
+ * The rows corresponding to node i are in the range
+ * [nodeOffsets(i), nodeOffsets(i+1)).
+ * @param activeNodes  Nodes which are active (still being split).
+ * Inactive nodes are known to be leaves in the final 
tree.
+ */
+private[impl] case class PartitionInfo(
+columns: Array[FeatureVector],
+nodeOffsets: Array[Int],
+activeNodes: BitSet,
+fullImpurityAggs: Array[ImpurityAggregatorSingle]) extends 
Serializable {
+
+  // pre-allocated temporary buffers that we use to sort
+  // instances in left and right children during update
+  val tempVals: Array[Int] = new Array[Int](columns(0).values.length)
+  val tempIndices: Array[Int] = new Array[Int](columns(0).values.length)
+
+  /** For debugging */
+  override def toString: String = {
+"PartitionInfo(" +
+  "  columns: {\n" +
+  columns.mkString(",\n") +
+  "  },\n" +
+  s"  nodeOffsets: ${nodeOffsets.mkString(", ")},\n" +
+  s"  activeNodes: ${activeNodes.iterator.mkString(", ")},\n" +
+  ")\n"
+  }
+
+  /**
+   * Update columns and nodeOffsets for the next level of the tree.
+   *
+   * Update columns:
+   *   For each column,
+   * For each (previously) active node,
+   *   Sort corresponding range of instances based on bit vector.
+   * Update nodeOffsets, activeNodes:
+   *   Split offsets for nodes which split (which can be identified using 
the bit vector).
+   *
+   * @param instanceBitVector  Bit vector encoding splits for the next 
level of the tree.
+   *These must follow a 2-level ordering, where the 
first level is by node
+   *and the second level is by row index.
+   *bitVector(i) = false iff instance i goes to the 
left child.
+   *For instances at inactive (leaf) nodes, the value 
can be arbitrary.
+   * @return Updated partition info
+   */
+  def update(
+  instanceBitVector: BitSet,
+  newNumNodeOffsets: Int,
+  labels: Array[Byte],
+  metadata: DecisionTreeMetadata): PartitionInfo = {
+// Create a 2-level representation of the new nodeOffsets (to be 
flattened).
+// These 2 levels correspond to original nodes and their children (if 
split).
+val newNodeOffsets = nodeOffsets.map(Array(_))
+val newFullImpurityAggs = fullImpurityAggs.map(Array(_))
+
+val newColumns = columns.zipWithIndex.map { case (col, index) =>
+  index match {
+case 0 => first(col, instanceBitVector, metadata,
+  labels, newNodeOffsets, newFullImpurityAggs)
+case _ => rest(col, instanceBitVector, newNodeOffsets)
+  }
+

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

2016-08-30 Thread jkbradley

Github user jkbradley commented on a diff in the pull request:

https://github.com/apache/spark/pull/14872#discussion_r76853460
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/tree/impl/PartitionInfo.scala ---
@@ -0,0 +1,422 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.apache.spark.util.collection.BitSet
+
+/**
+ * Intermediate data stored during learning.
+ * TODO(smurching): Rename; maybe TrainingInfo?
+ *
+ * Node indexing for nodeOffsets, activeNodes:
+ * Nodes are indexed left-to-right along the periphery of the tree, with 
0-based indices.
+ * The periphery is the set of leaf nodes (active and inactive).
+ *
+ * @param columns  Array of columns.
+ * Each column is sorted first by nodes (left-to-right 
along the tree periphery);
+ * all columns share this first level of sorting.
+ * Within each node's group, each column is sorted based 
on feature value;
+ * this second level of sorting differs across columns.
+ * @param nodeOffsets  Offsets into the columns indicating the first level 
of sorting (by node).
+ * The rows corresponding to node i are in the range
+ * [nodeOffsets(i), nodeOffsets(i+1)).
+ * @param activeNodes  Nodes which are active (still being split).
+ * Inactive nodes are known to be leaves in the final 
tree.
+ */
+private[impl] case class PartitionInfo(
+columns: Array[FeatureVector],
+nodeOffsets: Array[Int],
+activeNodes: BitSet,
+fullImpurityAggs: Array[ImpurityAggregatorSingle]) extends 
Serializable {
--- End diff --

Don't store this.  Just compute it when first choosing a split for a node, 
and reuse those stats as the fullImpurityAggs for all features for that node.


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[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

2016-08-30 Thread jkbradley

Github user jkbradley commented on a diff in the pull request:

https://github.com/apache/spark/pull/14872#discussion_r76853452
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/tree/impl/PartitionInfo.scala ---
@@ -0,0 +1,422 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.apache.spark.util.collection.BitSet
+
+/**
+ * Intermediate data stored during learning.
+ * TODO(smurching): Rename; maybe TrainingInfo?
+ *
+ * Node indexing for nodeOffsets, activeNodes:
+ * Nodes are indexed left-to-right along the periphery of the tree, with 
0-based indices.
+ * The periphery is the set of leaf nodes (active and inactive).
+ *
+ * @param columns  Array of columns.
+ * Each column is sorted first by nodes (left-to-right 
along the tree periphery);
+ * all columns share this first level of sorting.
+ * Within each node's group, each column is sorted based 
on feature value;
+ * this second level of sorting differs across columns.
+ * @param nodeOffsets  Offsets into the columns indicating the first level 
of sorting (by node).
+ * The rows corresponding to node i are in the range
+ * [nodeOffsets(i), nodeOffsets(i+1)).
+ * @param activeNodes  Nodes which are active (still being split).
+ * Inactive nodes are known to be leaves in the final 
tree.
+ */
+private[impl] case class PartitionInfo(
+columns: Array[FeatureVector],
+nodeOffsets: Array[Int],
+activeNodes: BitSet,
--- End diff --

not needed; use actual nodes


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[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

2016-08-30 Thread jkbradley

Github user jkbradley commented on a diff in the pull request:

https://github.com/apache/spark/pull/14872#discussion_r76853370
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/tree/impl/LocalDecisionTree.scala ---
@@ -0,0 +1,575 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.roaringbitmap.RoaringBitmap
+
+import org.apache.spark.ml.tree._
+import org.apache.spark.mllib.tree.model.ImpurityStats
+import org.apache.spark.util.collection.BitSet
+
+/** Object exposing methods for local training of decision trees */
+private[ml] object LocalDecisionTree {
+
+  /**
+   * Fully splits the passed-in node on the provided local dataset.
+   * TODO(smurching): Accept a seed for feature subsampling
+   *
+   * @param node LearningNode to split
+   */
+  def fitNode(
+  input: Array[BaggedPoint[TreePoint]],
+  node: LearningNode,
+  metadata: DecisionTreeMetadata,
+  splits: Array[Array[Split]]): Node = {
+
+// The case with 1 node (depth = 0) is handled separately.
+// This allows all iterations in the depth > 0 case to use the same 
code.
+// TODO: Check that learning works when maxDepth > 0 but learning 
stops at 1 node (because of
+//   other parameters).
+if (metadata.maxDepth == 0) {
+  val impurityAggregator: ImpurityAggregatorSingle =
+input.aggregate(metadata.createImpurityAggregator())(
+  (agg, lp) => agg.update(lp.datum.label, 1.0),
+  (agg1, agg2) => agg1.add(agg2))
+  val impurityCalculator = impurityAggregator.getCalculator
+  return new 
LeafNode(LocalDecisionTreeUtils.getPredict(impurityCalculator).predict,
+impurityCalculator.calculate(), impurityCalculator)
+}
+
+// Prepare column store.
+//   Note: rowToColumnStoreDense checks to make sure numRows < 
Int.MaxValue.
+val colStoreInit: Array[Array[Int]]
+  = 
LocalDecisionTreeUtils.rowToColumnStoreDense(input.map(_.datum.binnedFeatures))
+val labels = input.map(_.datum.label)
+
+// Train classifier if numClasses is between 1 and 32, otherwise fit a 
regression model
+// on the dataset
+if (metadata.numClasses > 1 && metadata.numClasses <= 32) {
+  throw new UnsupportedOperationException("Local training of a 
decision tree classifier is" +
+"unsupported; currently, only regression is supported")
+} else {
+  // TODO(smurching): Pass an array of instanceWeights extracted from 
the input BaggedPoint?
+  // Also, pass seed for feature subsampling
+  trainRegressor(node, colStoreInit, labels, metadata, splits)
+}
+  }
+
+  /**
+   * Locally fits a decision tree regressor.
+   * TODO(smurching): Logic for fitting a classifier & regressor seems the 
same; only difference
+   * is impurity metric. Use the same logic for fitting a classifier?
+   *
+   * @param rootNode Node to fit on the passed-in dataset
+   * @param colStoreInit Array of columns of training data
+   * @param metadata Metadata object
+   * @param splits splits(i) = Array of possible splits for feature i
+   * @return
+   */
+  def trainRegressor(
+  rootNode: LearningNode,
+  colStoreInit: Array[Array[Int]],
+  labels: Array[Double],
+  metadata: DecisionTreeMetadata,
+  splits: Array[Array[Split]]): Node = {
+
+// Sort each column by feature values.
+val colStore: Array[FeatureVector] = colStoreInit.zipWithIndex.map { 
case (col, featureIndex) =>
+  val featureArity: Int = 
metadata.featureArity.getOrElse(featureIndex, 0)
+  FeatureVector.fromOriginal(featureIndex, featureArity, col)
+}
+
+val numRows = colStore.headOption match {
+  case None => 0
+  case Some(column) => column.values.size
+}
+
+// Create an impurityAggregator object containing info

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

2016-08-30 Thread jkbradley

Github user jkbradley commented on a diff in the pull request:

https://github.com/apache/spark/pull/14872#discussion_r76853423
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/tree/impl/LocalDecisionTreeUtils.scala 
---
@@ -0,0 +1,259 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.roaringbitmap.RoaringBitmap
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel
+import org.apache.spark.ml.regression.DecisionTreeRegressionModel
+import org.apache.spark.ml.tree._
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
+import org.apache.spark.mllib.tree.impurity._
+import org.apache.spark.mllib.tree.model.{ImpurityStats, Predict}
+import org.apache.spark.util.collection.BitSet
+
+/**
+ * DecisionTree which partitions data by feature.
+ *
+ * Algorithm:
+ *  - Repartition data, grouping by feature.
+ *  - Prep data (sort continuous features).
+ *  - On each partition, initialize instance--node map with each instance 
at root node.
+ *  - Iterate, training 1 new level of the tree at a time:
+ * - On each partition, for each feature on the partition, select the 
best split for each node.
+ * - Aggregate best split for each node.
+ * - Aggregate bit vector (1 bit/instance) indicating whether each 
instance splits
+ *   left or right.
+ * - Broadcast bit vector.  On each partition, update instance--node 
map.
+ *
+ * TODO: Update to use a sparse column store.
+ */
+private[ml] object LocalDecisionTreeUtils extends Logging {
+
+  /**
+   * Convert a dataset of [[Vector]] from row storage to column storage.
+   * This can take any [[Vector]] type but stores data as [[DenseVector]].
+   *
+   * This maintains sparsity in the data.
+   *
+   * This maintains matrix structure.  I.e., each partition of the output 
RDD holds adjacent
+   * columns.  The number of partitions will be min(input RDD's number of 
partitions, numColumns).
+   *
+   * @param rowStore  An array of input data rows, each represented as an
+   *  int array of binned feature values
+   * @return Transpose of rowStore with
+   *
+   * TODO: Add implementation for sparse data.
+   *   For sparse data, distribute more evenly based on number of 
non-zeros.
+   *   (First collect stats to decide how to partition.)
+   */
+  private[impl] def rowToColumnStoreDense(rowStore: Array[Array[Int]]): 
Array[Array[Int]] = {
+// Compute the number of rows in the data
+val numRows = {
+  val longNumRows: Long = rowStore.size
+  require(longNumRows < Int.MaxValue, s"rowToColumnStore given RDD 
with $longNumRows rows," +
+s" but can handle at most ${Int.MaxValue} rows")
+  longNumRows.toInt
+}
+
+// Return an empty array for a dataset with zero rows or columns, 
otherwise
+// return the transpose of the rowStore matrix
+if (numRows == 0 || rowStore(0).size == 0) {
+  Array.empty
+} else {
+  val numCols = rowStore(0).size
+  0.until(numCols).map { colIdx =>
+rowStore.map(row => row(colIdx))
+  }.toArray
+}
+  }
+
+  private[impl] def finalizeTree(
+  rootNode: Node,
+  algo: OldAlgo.Algo,
+  numClasses: Int,
+  numFeatures: Int,
+  parentUID: Option[String]): DecisionTreeModel = {
+parentUID match {
+  case Some(uid) =>
+if (algo == OldAlgo.Classification) {
+  new DecisionTreeClassificationModel(uid, rootNode, numFeatures = 
numFeatures,
+numClasses = numClasses)
+} else {
+  new DecisionTreeRegressionModel(uid, rootNode, numFeatures = 
numFeatures)
+}
+  case None =>
+if (algo ==

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

2016-08-30 Thread jkbradley

Github user jkbradley commented on a diff in the pull request:

https://github.com/apache/spark/pull/14872#discussion_r76853386
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/tree/impl/LocalDecisionTree.scala ---
@@ -0,0 +1,575 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.roaringbitmap.RoaringBitmap
+
+import org.apache.spark.ml.tree._
+import org.apache.spark.mllib.tree.model.ImpurityStats
+import org.apache.spark.util.collection.BitSet
+
+/** Object exposing methods for local training of decision trees */
+private[ml] object LocalDecisionTree {
+
+  /**
+   * Fully splits the passed-in node on the provided local dataset.
+   * TODO(smurching): Accept a seed for feature subsampling
+   *
+   * @param node LearningNode to split
+   */
+  def fitNode(
+  input: Array[BaggedPoint[TreePoint]],
+  node: LearningNode,
+  metadata: DecisionTreeMetadata,
+  splits: Array[Array[Split]]): Node = {
+
+// The case with 1 node (depth = 0) is handled separately.
+// This allows all iterations in the depth > 0 case to use the same 
code.
+// TODO: Check that learning works when maxDepth > 0 but learning 
stops at 1 node (because of
+//   other parameters).
+if (metadata.maxDepth == 0) {
+  val impurityAggregator: ImpurityAggregatorSingle =
+input.aggregate(metadata.createImpurityAggregator())(
+  (agg, lp) => agg.update(lp.datum.label, 1.0),
+  (agg1, agg2) => agg1.add(agg2))
+  val impurityCalculator = impurityAggregator.getCalculator
+  return new 
LeafNode(LocalDecisionTreeUtils.getPredict(impurityCalculator).predict,
+impurityCalculator.calculate(), impurityCalculator)
+}
+
+// Prepare column store.
+//   Note: rowToColumnStoreDense checks to make sure numRows < 
Int.MaxValue.
+val colStoreInit: Array[Array[Int]]
+  = 
LocalDecisionTreeUtils.rowToColumnStoreDense(input.map(_.datum.binnedFeatures))
+val labels = input.map(_.datum.label)
+
+// Train classifier if numClasses is between 1 and 32, otherwise fit a 
regression model
+// on the dataset
+if (metadata.numClasses > 1 && metadata.numClasses <= 32) {
+  throw new UnsupportedOperationException("Local training of a 
decision tree classifier is" +
+"unsupported; currently, only regression is supported")
+} else {
+  // TODO(smurching): Pass an array of instanceWeights extracted from 
the input BaggedPoint?
+  // Also, pass seed for feature subsampling
+  trainRegressor(node, colStoreInit, labels, metadata, splits)
+}
+  }
+
+  /**
+   * Locally fits a decision tree regressor.
+   * TODO(smurching): Logic for fitting a classifier & regressor seems the 
same; only difference
+   * is impurity metric. Use the same logic for fitting a classifier?
+   *
+   * @param rootNode Node to fit on the passed-in dataset
+   * @param colStoreInit Array of columns of training data
+   * @param metadata Metadata object
+   * @param splits splits(i) = Array of possible splits for feature i
+   * @return
+   */
+  def trainRegressor(
+  rootNode: LearningNode,
+  colStoreInit: Array[Array[Int]],
+  labels: Array[Double],
+  metadata: DecisionTreeMetadata,
+  splits: Array[Array[Split]]): Node = {
+
+// Sort each column by feature values.
+val colStore: Array[FeatureVector] = colStoreInit.zipWithIndex.map { 
case (col, featureIndex) =>
+  val featureArity: Int = 
metadata.featureArity.getOrElse(featureIndex, 0)
+  FeatureVector.fromOriginal(featureIndex, featureArity, col)
+}
+
+val numRows = colStore.headOption match {
+  case None => 0
+  case Some(column) => column.values.size
+}
+
+// Create an impurityAggregator object containing info

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

2016-08-30 Thread jkbradley

Github user jkbradley commented on a diff in the pull request:

https://github.com/apache/spark/pull/14872#discussion_r76853239
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/tree/impl/LocalDecisionTree.scala ---
@@ -0,0 +1,575 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.roaringbitmap.RoaringBitmap
+
+import org.apache.spark.ml.tree._
+import org.apache.spark.mllib.tree.model.ImpurityStats
+import org.apache.spark.util.collection.BitSet
+
+/** Object exposing methods for local training of decision trees */
+private[ml] object LocalDecisionTree {
+
+  /**
+   * Fully splits the passed-in node on the provided local dataset.
+   * TODO(smurching): Accept a seed for feature subsampling
+   *
+   * @param node LearningNode to split
+   */
+  def fitNode(
+  input: Array[BaggedPoint[TreePoint]],
+  node: LearningNode,
+  metadata: DecisionTreeMetadata,
+  splits: Array[Array[Split]]): Node = {
+
+// The case with 1 node (depth = 0) is handled separately.
+// This allows all iterations in the depth > 0 case to use the same 
code.
+// TODO: Check that learning works when maxDepth > 0 but learning 
stops at 1 node (because of
+//   other parameters).
+if (metadata.maxDepth == 0) {
+  val impurityAggregator: ImpurityAggregatorSingle =
+input.aggregate(metadata.createImpurityAggregator())(
+  (agg, lp) => agg.update(lp.datum.label, 1.0),
+  (agg1, agg2) => agg1.add(agg2))
+  val impurityCalculator = impurityAggregator.getCalculator
+  return new 
LeafNode(LocalDecisionTreeUtils.getPredict(impurityCalculator).predict,
+impurityCalculator.calculate(), impurityCalculator)
+}
+
+// Prepare column store.
+//   Note: rowToColumnStoreDense checks to make sure numRows < 
Int.MaxValue.
+val colStoreInit: Array[Array[Int]]
+  = 
LocalDecisionTreeUtils.rowToColumnStoreDense(input.map(_.datum.binnedFeatures))
+val labels = input.map(_.datum.label)
+
+// Train classifier if numClasses is between 1 and 32, otherwise fit a 
regression model
+// on the dataset
+if (metadata.numClasses > 1 && metadata.numClasses <= 32) {
+  throw new UnsupportedOperationException("Local training of a 
decision tree classifier is" +
+"unsupported; currently, only regression is supported")
+} else {
+  // TODO(smurching): Pass an array of instanceWeights extracted from 
the input BaggedPoint?
+  // Also, pass seed for feature subsampling
+  trainRegressor(node, colStoreInit, labels, metadata, splits)
+}
+  }
+
+  /**
+   * Locally fits a decision tree regressor.
+   * TODO(smurching): Logic for fitting a classifier & regressor seems the 
same; only difference
+   * is impurity metric. Use the same logic for fitting a classifier?
+   *
+   * @param rootNode Node to fit on the passed-in dataset
+   * @param colStoreInit Array of columns of training data
+   * @param metadata Metadata object
+   * @param splits splits(i) = Array of possible splits for feature i
+   * @return
+   */
+  def trainRegressor(
+  rootNode: LearningNode,
+  colStoreInit: Array[Array[Int]],
+  labels: Array[Double],
+  metadata: DecisionTreeMetadata,
+  splits: Array[Array[Split]]): Node = {
+
+// Sort each column by feature values.
+val colStore: Array[FeatureVector] = colStoreInit.zipWithIndex.map { 
case (col, featureIndex) =>
+  val featureArity: Int = 
metadata.featureArity.getOrElse(featureIndex, 0)
+  FeatureVector.fromOriginal(featureIndex, featureArity, col)
+}
+
+val numRows = colStore.headOption match {
+  case None => 0
+  case Some(column) => column.values.size
+}
+
+// Create an impurityAggregator object containing info

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

[GitHub] spark pull request #14872: [SPARK-3162][MLlib][WIP] Add local tree training ...

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